Description
Question 12.1
Describe a situation or problem from your job, everyday life, current events, etc., for which a design of
experiments approach would be appropriate.
Question 12.2
To determine the value of 10 different yes/no features to the market value of a house (large yard, solar
roof, etc.), a real estate agent plans to survey 50 potential buyers, showing a fictitious house with
different combinations of features. To reduce the survey size, the agent wants to show just 16 fictitious
houses. Use R’s FrF2 function (in the FrF2 package) to find a fractional factorial design for this
experiment: what set of features should each of the 16 fictitious houses? Note: the output of FrF2 is
“1” (include) or “-1” (don’t include) for each feature.
Question 13.1
For each of the following distributions, give an example of data that you would expect to follow this
distribution (besides the examples already discussed in class).
a. Binomial
b. Geometric
c. Poisson
d. Exponential
e. Weibull
Question 13.2
In this problem you, can simulate a simplified airport security system at a busy airport. Passengers arrive
according to a Poisson distribution with λ1 = 5 per minute (i.e., mean interarrival rate µ1 = 0.2 minutes)
to the ID/boarding-pass check queue, where there are several servers who each have exponential
service time with mean rate µ2 = 0.75 minutes. [Hint: model them as one block that has more than one
resource.] After that, the passengers are assigned to the shortest of the several personal-check queues,
where they go through the personal scanner (time is uniformly distributed between 0.5 minutes and 1
minute).
Use the Arena software (PC users) or Python with SimPy (PC or Mac users) to build a simulation of the
system, and then vary the number of ID/boarding-pass checkers and personal-check queues to
determine how many are needed to keep average wait times below 15 minutes. [If you’re using SimPy,
or if you have access to a non-student version of Arena, you can use λ1 = 50 to simulate a busier airport.]